RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.65.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can Vicuna 7B run on Tesla P100 16GB?
YES — Tight Fit
C54Usable
Estimated from fit model
Vicuna 7B needs ~14.9 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 14.9 GB, 98.0 tok/s, Tight fit
14.9 GB required16.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
4K
Memory
14.9 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsVicuna 7B on Tesla P100 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 98.0 tok/s | 1078 ms | 4K |
| Coding | C | Tight fit | 98.0 tok/s | 1976 ms | 4K |
| Agentic Coding | F | Too heavy | 34.2 tok/s | 8224 ms | 4K |
| Reasoning | C | Tight fit | 98.0 tok/s | 2335 ms | 4K |
| RAG | F | Too heavy | 34.2 tok/s | 10280 ms | 4K |
Quantization options
How Vicuna 7B (7B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C47 |
Q3_K_S | 3 | 3.4 GB | Low | C48 |
NVFP4 | 4 | 3.9 GB | Medium | C48 |
Q4_K_M | 4 | 4.3 GB | Medium | C49 |
Q5_K_M | 5 | 5.0 GB | High | C49 |
Q6_K | 6 | 5.7 GB | High | C50 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaアップグレードオプション
Vicuna 7Bを快適に動かすハードウェア
RTX 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+204)
B
Adds memory headroom for longer context windows and future model growth.98 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)
B
Adds memory headroom for longer context windows and future model growth.98 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can Tesla P100 16GB run Vicuna 7B?
Yes, Tesla P100 16GB can run Vicuna 7B with a C grade (Tight fit). Expected decode speed: 98.0 tok/s.
How much VRAM does Vicuna 7B need?
Vicuna 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Vicuna 7B?
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Vicuna 7B run at on Tesla P100 16GB?
On Tesla P100 16GB, Vicuna 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can Tesla P100 16GB run Vicuna 7B for coding?
For coding workloads, Vicuna 7B on Tesla P100 16GB receives a C grade with 98.0 tok/s and 4K context.
What context window can Vicuna 7B use on Tesla P100 16GB?
On Tesla P100 16GB, Vicuna 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
What should I upgrade first if Vicuna 7B feels slow on Tesla P100 16GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/vicuna-7b-on-tesla-p100-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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